Development of a Predictive Model for Large‐Volume Pleural Effusion After Separation Surgery in Patients With Spinal Metastatic Tumors

TL;DR

This study develops a predictive model to identify patients at risk of large-volume pleural effusion after spinal tumor surgery, aiming to improve treatment outcomes.

Contribution

A high-accuracy predictive nomogram for large-volume pleural effusion after separation surgery in spinal metastasis patients is developed.

Findings

Advanced age, intraoperative blood loss, and low preoperative albumin are significant risk factors for large-volume pleural effusion.

The predictive model achieved high accuracy with AUC values of 0.953 (training) and 0.927 (testing).

Early identification of high-risk patients can improve prognosis by enabling timely interventions.

Abstract

Separation surgery followed by radiotherapy has emerged as a prevalent approach for managing spinal metastatic tumors. However, large‐volume postoperative pleural effusion (POPE) represents a challenging complication, as it potentially delays subsequent treatments and increases morbidity. This study aims to identify risk factors for large‐volume POPE and develop a predictive model for early identification to improve patient prognosis.

This retrospective study analyzed 443 patients who underwent separation surgery for spinal metastases at our center between January 2014 and January 2022. High‐resolution CT‐based 3D modeling was utilized for accurate pleural effusion (PE) volume quantification. Variables including patient demographics, surgical details, and laboratory results were examined to identify risk factors associated with large‐volume POPE (≥ 1000 mL). A predictive nomogram was developed based on the multivariate logistic regression analysis.

Our findings indicated that advanced age, increased intraoperative blood loss, and decreased levels of preoperative serum albumin, postoperative serum protein, and hemoglobin were significant independent risk factors for large‐volume POPE. The predictive nomogram demonstrated high accuracy, with a mean AUC value of 0.953 for the training dataset and 0.927 for the testing dataset, indicating reliable predictability for identifying patients at high risk for large‐volume POPE.

Our study identified independent risk factors for large‐volume PE following separation surgery in patients with spinal metastasis. The developed nomogram offers a practical tool for early identification of high‐risk groups, enabling timely and targeted interventions. By reducing the risk of large POPE, this approach may shorten hospitalization and accelerate the resumption of postoperative treatment, ultimately improving patient prognosis.

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